Identifying at-risk patients from clinical data

James Stewart Briggs

Identifying at-risk patients from clinical data

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Abstract

Recent cases such as the Mid-Staffordshire enquiry have highlighted the importance of mechanisms to support the delivery of safe care to patients in hospitals. Part of this is to spot patients whose condition is deteriorating while it is still early enough to prevent an adverse outcome such as cardiac arrest or death. This talk will describe our risk models (one based on blood tests; one on vital signs) and how the vital signs one has been incorporated into routine hospital practice in the UK.

Original language	English
Number of pages	23
Publication status	Published - 7 Sept 2015
Event	MEIbioeng 2015 - University of Leeds, Leeds, United Kingdom Duration: 7 Sept 2015 → 8 Sept 2015

Conference

Conference	MEIbioeng 2015
Country/Territory	United Kingdom
City	Leeds
Period	7/09/15 → 8/09/15

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How software drives safety in aerospace, healthcare, oil and gas
Jim Briggs
1/01/16
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abstract = "Recent cases such as the Mid-Staffordshire enquiry have highlighted the importance of mechanisms to support the delivery of safe care to patients in hospitals. Part of this is to spot patients whose condition is deteriorating while it is still early enough to prevent an adverse outcome such as cardiac arrest or death. This talk will describe our risk models (one based on blood tests; one on vital signs) and how the vital signs one has been incorporated into routine hospital practice in the UK.",

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AB - Recent cases such as the Mid-Staffordshire enquiry have highlighted the importance of mechanisms to support the delivery of safe care to patients in hospitals. Part of this is to spot patients whose condition is deteriorating while it is still early enough to prevent an adverse outcome such as cardiac arrest or death. This talk will describe our risk models (one based on blood tests; one on vital signs) and how the vital signs one has been incorporated into routine hospital practice in the UK.

M3 - Paper

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Y2 - 7 September 2015 through 8 September 2015

ER -

Identifying at-risk patients from clinical data

Abstract

Conference

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How software drives safety in aerospace, healthcare, oil and gas

Cite this